报告题目：Learning Robust Decision Rules for Censored and Confounded Data

报 告 人：崔逸凡 长聘副教授（研究员） 浙江大学

报告时间：2026年1月14日 10：00-11：00

报告地点：腾讯会议613806501

校内联系人：王培洁 [email protected]

报告摘要：In this talk, we propose two robust criteria for learning optimal treatment rules with censored survival outcomes. The first one aims to identify a treatment rule that maximizes the truncated mean survival time, where the threshold is specified by a given quantile such as the median; the second one focuses on maximizing buffered survival probabilities, with the threshold adaptively adjusted to account for the truncated mean survival time. Moreover, we develop robust treatment rules that enable reliable policy recommendations when unmeasured confounding is present, using the proximal causal inference framework. Simulation studies and real-world applications demonstrate the superior performance of the proposed methods.

报告人简介：崔逸凡，浙江大学长聘副教授（研究员），博士生导师。北卡罗来纳大学教堂山分校统计与运筹专业博士，曾任宾夕法尼亚大学沃顿商娱乐城博士后研究员、新加坡国立大学统计与数据科学系助理教授。国家级青年人才计划入选者（2021）。